1 About

Paper prepared for the 2021 International Conference on Evolving Cities, University of Southampton, 22 – 24 September 2021.

2 Citation

Anderson, B. (2021). Simulating the consequences of an emissions levy at the city and neighbourhood scale. Paper presented at International Conference on Evolving Cities, Southampton, United Kingdom

3 Introduction

Background blurb about emissions, retofit, carbon tax/levy etc

4 Emissions Levy Case Study - All LSOAs

In the reminder of this paper we develop a model of an emissions levy using LSOA level data on emissions derived from the CREDS place-based emissions calculator. We apply carbon ‘values’ to a number of emissions categories to estimate the levy revenue that would be generated for each LSOA in year 1 of such a levy. We then sum these values to given an overall levy revenue estimate for the area in the case study.

We then use estimates of the cost of retrofitting EPC band dwellings D-E and F-G together with estimates of the number of such dwellings in each of the LSOAs to calculate the likely cost of such upgrades in each LSOA and for the whole area in the case study.

We then compare the distributions of the two to understand whether sufficient revenue would be generated within each LSOA to enable the per-LSOA or whole case study area costs of the energy efficiency upgrades to be met. In doing so we also analyse the extent to which redistribution of revenue from high emissions areas (households) would be required.

It should be noted that this is area level analysis using mean emissions per household. It will not capture within-LSOA hetergeneity in emissions and so will almost certaonly underestimate the range of the household level emissions levy value.

NB: no maps in the interests of speed

4.1 Data

We will use a number of datasets to analyse the patterns of emissions. Some of these are in the repo as they are public access, others are not (or too large).

This analysis is at LSOA level.

4.1.1 Useful LSOA labels and codes

Load lSOA look-up table. This covers all countries/regions of the UK but some variables are not defined in some countries.

## Loading LSOA look-up table with useful labels

## [1] 42619

Table 4.1: Data summary
Name	data$lsoa_lookup
Number of rows	42619
Number of columns	22
Key	NULL
_______________________
Column type frequency:
character	21
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	complete_rate	min	max	empty	n_unique
LSOA11CD	1	8	9	0	42619
LSOA11NM	1	0	33	7866	34754
MSOA11CD	1	0	9	7866	7202
MSOA11NM	1	0	32	7866	7202
LA11CD	1	0	9	7866	337
LA11NM	1	0	35	7866	337
WD20CD	1	0	9	7866	8024
WD20NM	1	0	53	7866	7438
LAD20CD	1	0	9	7866	337
LAD20NM	1	0	35	7866	337
i.LSOA11NM	1	0	33	7866	34754
RUC11CD	1	0	2	7866	9
RUC11	1	0	47	7866	9
SOA Code	1	8	9	0	42619
SOA Name	1	3	63	0	42465
LA Code	1	9	9	0	391
LA Name	1	4	36	0	391
Region/Country	1	4	24	0	12
Supergroup Name	1	15	35	0	8
Group Code	1	2	2	0	24
Group Name	1	12	35	0	24

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
Supergroup Code	0	1	4.76	2.1	1	3	5	7	8	▃▃▇▁▆

##                                                  
##                                                   East East Midlands London
##                                                      0             0      0
##   Rural town and fringe                            544           417      4
##   Rural town and fringe in a sparse setting         19             4      0
##   Rural village and dispersed                      448           273      4
##   Rural village and dispersed in a sparse setting   12            11      0
##   Urban city and town                             2232          1540     17
##   Urban city and town in a sparse setting            0             6      0
##   Urban major conurbation                          359            20   4810
##   Urban minor conurbation                            0           503      0
##   <NA>                                               0             0      0
##                                                  
##                                                   North East North West
##                                                            0          0
##   Rural town and fringe                                  201        235
##   Rural town and fringe in a sparse setting               20         21
##   Rural village and dispersed                             48        147
##   Rural village and dispersed in a sparse setting         22         38
##   Urban city and town                                    620       1550
##   Urban city and town in a sparse setting                  9         11
##   Urban major conurbation                                737       2495
##   Urban minor conurbation                                  0          0
##   <NA>                                                     0          0
##                                                  
##                                                   Northern Ireland Scotland
##                                                                890     6976
##   Rural town and fringe                                          0        0
##   Rural town and fringe in a sparse setting                      0        0
##   Rural village and dispersed                                    0        0
##   Rural village and dispersed in a sparse setting                0        0
##   Urban city and town                                            0        0
##   Urban city and town in a sparse setting                        0        0
##   Urban major conurbation                                        0        0
##   Urban minor conurbation                                        0        0
##   <NA>                                                           0        0
##                                                  
##                                                   South East South West Wales
##                                                            0          0     0
##   Rural town and fringe                                  579        419   252
##   Rural town and fringe in a sparse setting                0         29    78
##   Rural village and dispersed                            495        493   129
##   Rural village and dispersed in a sparse setting          0         51   147
##   Urban city and town                                   3848       2274  1268
##   Urban city and town in a sparse setting                  0         15    35
##   Urban major conurbation                                460          0     0
##   Urban minor conurbation                                  0          0     0
##   <NA>                                                     0          0     0
##                                                  
##                                                   West Midlands
##                                                               0
##   Rural town and fringe                                     228
##   Rural town and fringe in a sparse setting                   5
##   Rural village and dispersed                               264
##   Rural village and dispersed in a sparse setting            19
##   Urban city and town                                      1381
##   Urban city and town in a sparse setting                     9
##   Urban major conurbation                                  1581
##   Urban minor conurbation                                     0
##   <NA>                                                        0
##                                                  
##                                                   Yorkshire and The Humber <NA>
##                                                                          0    0
##   Rural town and fringe                                                310    0
##   Rural town and fringe in a sparse setting                             21    0
##   Rural village and dispersed                                          189    0
##   Rural village and dispersed in a sparse setting                       28    0
##   Urban city and town                                                  994    0
##   Urban city and town in a sparse setting                                9    0
##   Urban major conurbation                                             1061    0
##   Urban minor conurbation                                              705    0
##   <NA>                                                                   0    0

4.1.2 IMD 2019

Labeled as 2019 but actually 2018 data. Source: https://www.gov.uk/government/statistics/english-indices-of-deprivation-2019

## Overall IMD decile counts

## [1] 32844

## 
##   1 (10% most deprived)                       2                       3 
##                    3284                    3284                    3285 
##                       4                       5                       6 
##                    3284                    3285                    3284 
##                       7                       8                       9 
##                    3284                    3285                    3284 
## 10 (10% least deprived) 
##                    3285

## # Southampton IMD decile counts

## [1] 32844

## 
##   1 (10% most deprived)                       2                       3 
##                    3284                    3284                    3285 
##                       4                       5                       6 
##                    3284                    3285                    3284 
##                       7                       8                       9 
##                    3284                    3285                    3284 
## 10 (10% least deprived) 
##                    3285

## 
##   1 (10% most deprived)                       2                       3 
##              0.09998782              0.09998782              0.10001827 
##                       4                       5                       6 
##              0.09998782              0.10001827              0.09998782 
##                       7                       8                       9 
##              0.09998782              0.10001827              0.09998782 
## 10 (10% least deprived) 
##              0.10001827

## 
## 50% least deprived  50% most deprived 
##              16422              16422

## 
## 50% least deprived  50% most deprived 
##                0.5                0.5

These are LSOA level deprivation indices. Decile is the English & Welsh decile:

1 = 10% most deprived LSOAs in England & Wales;
10 = 10% least deprived LSOA in England & Wales.

4.1.3 Fuel poverty

2019 estimates - do we actually use this data?

Source: https://www.gov.uk/government/statistics/sub-regional-fuel-poverty-data-2021

4.2 CREDS place-based emmissions estimates

See https://www.creds.ac.uk/why-we-built-a-place-based-carbon-calculator/

“The highest carbon areas have an average per person footprint more than eight times larger than the lowest carbon areas.”

“We are not effectively targeting decarbonisation policies in high carbon areas. For example, the recently collapsed Green Homes Grants scheme provided a grant to cover 66% of the cost (up to £5,000) of retrofitting homes. For people claiming certain benefits, the cap was raised to 100% and £10,000. But the calculator shows that the big polluters are the large homes in very wealthy areas. In these neighbourhoods, the issue is not affordability but motivation. For high income households, energy costs are a small proportion of their expenditure and so the cost savings for retrofitting their home are inconsequential. As there are no policy “sticks” to incentivise action in the collective interest it is unsurprising that high carbon neighbourhoods have not prioritised decarbonisation."

Source: https://www.carbon.place/

Notes:

Emissions are presented as per capita…
Appears to be based on residential/citizen emissions only - does not appear to include commercial/manufacturing/land use etc

## [1] 32844

Table 4.2: Data summary
Name	credsLsoaDT
Number of rows	32844
Number of columns	96
Key	LSOA11CD
_______________________
Column type frequency:
character	16
factor	1
numeric	79
________________________
Group variables	None

Variable type: character

skim_variable	complete_rate	min	max	n_unique
lsoa11cd	1	9	9	32844
lsoa11nm	1	9	33	32844
lsoa11nmw	1	9	33	32844
LSOA01NM	1	9	33	32844
LADcd	1	9	9	317
LADnm	1	4	35	317
LAD11NM	1	4	35	317
IMD_50pc	1	17	18	2
LSOA11CD	1	9	9	32844
LSOA11NM	1	9	33	32844
WD20CD	1	9	9	7180
RUC11	1	19	47	8
oacSuperGroupName	1	15	35	8
region	1	4	24	9
i.LAD11NM	1	4	28	326
WD18NM	1	3	56	6786

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
IMD_Decile_label	0	1	FALSE	10	3: 3285, 5: 3285, 8: 3285, 10 : 3285

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
FID	0	1.00	16422.50	9481.39	1.00	8211.75	16422.50	24633.25	3.284400e+04	▇▇▇▇▇
st_areasha	0	1.00	3971258.76	13341704.03	16901.71	272676.31	455693.68	1285547.87	6.837843e+08	▇▁▁▁▁
st_lengths	0	1.00	8047.21	10508.84	681.05	3117.18	4282.31	7239.18	1.693713e+05	▇▁▁▁▁
IMD_Rank	0	1.00	16422.50	9481.39	1.00	8211.75	16422.50	24633.25	3.284400e+04	▇▇▇▇▇
IMD_Decile	0	1.00	5.50	2.87	1.00	3.00	5.50	8.00	1.000000e+01	▇▇▇▇▇
IMDScore	0	1.00	21.67	15.33	0.54	9.91	17.65	29.58	9.274000e+01	▇▅▂▁▁
IMDRank0	0	1.00	16422.50	9481.39	1.00	8211.75	16422.50	24633.25	3.284400e+04	▇▇▇▇▇
IMDDec0	0	1.00	5.50	2.87	1.00	3.00	5.50	8.00	1.000000e+01	▇▇▇▇▇
IncScore	0	1.00	0.13	0.09	0.00	0.06	0.10	0.18	6.100000e-01	▇▃▂▁▁
IncRank	0	1.00	16422.50	9481.39	1.00	8211.75	16422.50	24633.25	3.284400e+04	▇▇▇▇▇
IncDec	0	1.00	5.50	2.87	1.00	3.00	5.50	8.00	1.000000e+01	▇▇▇▇▇
EmpScore	0	1.00	0.10	0.07	0.00	0.05	0.08	0.13	5.300000e-01	▇▃▁▁▁
EmpRank	0	1.00	16422.50	9481.39	1.00	8211.75	16422.50	24633.25	3.284400e+04	▇▇▇▇▇
EmpDec	0	1.00	5.50	2.87	1.00	3.00	5.50	8.00	1.000000e+01	▇▇▇▇▇
EduScore	0	1.00	21.69	18.61	0.01	7.36	16.18	30.91	9.945000e+01	▇▃▂▁▁
EduRank	0	1.00	16422.50	9481.39	1.00	8211.75	16422.50	24633.25	3.284400e+04	▇▇▇▇▇
EduDec	0	1.00	5.50	2.87	1.00	3.00	5.50	8.00	1.000000e+01	▇▇▇▇▇
HDDScore	0	1.00	0.00	0.86	-3.21	-0.59	-0.03	0.58	3.550000e+00	▁▃▇▂▁
HDDRank	0	1.00	16422.50	9481.39	1.00	8211.75	16422.50	24633.25	3.284400e+04	▇▇▇▇▇
HDDDec	0	1.00	5.50	2.87	1.00	3.00	5.50	8.00	1.000000e+01	▇▇▇▇▇
CriScore	0	1.00	0.00	0.82	-3.46	-0.56	0.02	0.56	3.350000e+00	▁▂▇▃▁
CriRank	0	1.00	16422.50	9481.39	1.00	8211.75	16422.50	24633.25	3.284400e+04	▇▇▇▇▇
CriDec	0	1.00	5.50	2.87	1.00	3.00	5.50	8.00	1.000000e+01	▇▇▇▇▇
BHSScore	0	1.00	21.69	10.71	0.48	13.66	20.20	28.27	7.046000e+01	▅▇▃▁▁
BHSRank	0	1.00	16422.50	9481.39	1.00	8211.75	16422.50	24633.25	3.284400e+04	▇▇▇▇▇
BHSDec	0	1.00	5.50	2.87	1.00	3.00	5.50	8.00	1.000000e+01	▇▇▇▇▇
EnvScore	0	1.00	21.69	15.20	0.13	9.45	18.51	31.08	9.160000e+01	▇▆▂▁▁
EnvRank	0	1.00	16422.50	9481.39	1.00	8211.75	16422.50	24633.25	3.284400e+04	▇▇▇▇▇
EnvDec	0	1.00	5.50	2.87	1.00	3.00	5.50	8.00	1.000000e+01	▇▇▇▇▇
IDCScore	0	1.00	0.16	0.12	0.00	0.06	0.13	0.23	9.000000e-01	▇▃▁▁▁
IDCRank	0	1.00	16422.50	9481.39	1.00	8211.75	16422.50	24633.25	3.284400e+04	▇▇▇▇▇
IDCDec	0	1.00	5.50	2.87	1.00	3.00	5.50	8.00	1.000000e+01	▇▇▇▇▇
IDOScore	0	1.00	0.17	0.12	0.01	0.07	0.13	0.23	9.900000e-01	▇▃▁▁▁
IDORank	0	1.00	16422.50	9481.39	1.00	8211.75	16422.50	24633.25	3.284400e+04	▇▇▇▇▇
IDODec	0	1.00	5.50	2.87	1.00	3.00	5.50	8.00	1.000000e+01	▇▇▇▇▇
CYPScore	0	1.00	0.00	0.80	-2.79	-0.55	-0.02	0.55	3.400000e+00	▁▅▇▂▁
CYPRank	0	1.00	16422.50	9481.39	1.00	8211.75	16422.50	24633.25	3.284400e+04	▇▇▇▇▇
CYPDec	0	1.00	5.50	2.87	1.00	3.00	5.50	8.00	1.000000e+01	▇▇▇▇▇
ASScore	0	1.00	0.31	0.11	0.03	0.22	0.30	0.38	7.500000e-01	▂▇▆▂▁
ASRank	0	1.00	16422.50	9481.39	1.00	8211.75	16422.50	24633.25	3.284400e+04	▇▇▇▇▇
ASDec	0	1.00	5.50	2.87	1.00	3.00	5.50	8.00	1.000000e+01	▇▇▇▇▇
GBScore	0	1.00	0.00	0.79	-2.76	-0.54	-0.04	0.48	3.260000e+00	▁▅▇▂▁
GBRank	0	1.00	16422.50	9481.39	1.00	8211.75	16422.50	24633.25	3.284400e+04	▇▇▇▇▇
GBDec	0	1.00	5.50	2.87	1.00	3.00	5.50	8.00	1.000000e+01	▇▇▇▇▇
WBScore	0	1.00	0.00	2.49	-8.45	-1.75	-0.23	1.60	7.660000e+00	▁▃▇▃▁
WBRank	0	1.00	16422.50	9481.39	1.00	8211.75	16422.50	24633.25	3.284400e+04	▇▇▇▇▇
WBDec	0	1.00	5.50	2.87	1.00	3.00	5.50	8.00	1.000000e+01	▇▇▇▇▇
IndScore	0	1.00	0.00	0.84	-3.36	-0.56	0.00	0.58	2.960000e+00	▁▂▇▅▁
IndRank	0	1.00	16422.50	9481.39	1.00	8211.75	16422.50	24633.25	3.284400e+04	▇▇▇▇▇
IndDec	0	1.00	5.50	2.87	1.00	3.00	5.50	8.00	1.000000e+01	▇▇▇▇▇
OutScore	0	1.00	0.00	0.82	-3.75	-0.58	-0.07	0.52	3.310000e+00	▁▂▇▃▁
OutRank	0	1.00	16422.50	9481.39	1.00	8211.75	16422.50	24633.25	3.284400e+04	▇▇▇▇▇
OutDec	0	1.00	5.50	2.87	1.00	3.00	5.50	8.00	1.000000e+01	▇▇▇▇▇
TotPop	0	1.00	1666.31	363.62	523.00	1446.00	1598.00	1800.00	9.551000e+03	▇▁▁▁▁
DepChi	0	1.00	316.80	117.76	17.00	238.00	298.00	372.00	1.632000e+03	▇▃▁▁▁
Pop16_59	0	1.00	965.48	306.56	310.00	784.00	907.00	1074.00	8.608000e+03	▇▁▁▁▁
Pop60_	0	1.00	384.02	151.87	15.00	276.00	367.00	471.00	1.372000e+03	▃▇▂▁▁
WorkPop	0	1.00	971.46	304.15	329.25	793.25	910.75	1076.00	8.588750e+03	▇▁▁▁▁
Shape__Area	0	1.00	10764015.16	37736149.72	43529.25	730662.72	1230811.81	3473220.26	2.098972e+09	▇▁▁▁▁
Shape__Length	0	1.00	13198.60	17330.35	1094.76	5107.38	7029.59	11916.91	2.974864e+05	▇▁▁▁▁
CREDStotal_kgco2e	0	1.00	13700570.11	5146064.38	4451600.00	9623500.00	13444900.00	16808000.00	2.366700e+08	▇▁▁▁▁
CREDSgas_kgco2e2018	805	0.98	1764087.16	562937.11	4110.90	1429190.00	1723600.00	2059200.00	6.798010e+06	▂▇▁▁▁
CREDSelec_kgco2e2018	0	1.00	761673.41	261403.26	21004.00	596490.00	690300.00	848175.00	4.586400e+06	▇▂▁▁▁
CREDSotherEnergy_kgco2e2011	0	1.00	168652.27	356823.87	0.00	28362.00	51800.00	108112.50	3.840000e+06	▇▁▁▁▁
CREDSallHomeEnergy_kgco2e2011	805	0.98	2775450.91	824951.54	674000.80	2239496.00	2602296.00	3112414.00	1.190016e+07	▇▃▁▁▁
CREDScar_kgco2e2018	0	1.00	1594080.93	702539.82	97520.00	1113480.00	1509600.00	1964700.00	1.272180e+07	▇▁▁▁▁
CREDSvan_kgco2e2018	2	1.00	273550.72	1976936.38	28.52	96537.00	154530.00	261690.00	2.237900e+08	▇▁▁▁▁
pop_2018	0	1.00	1704.35	426.91	591.00	1450.00	1620.00	1850.00	1.470000e+04	▇▁▁▁▁
energy_pc	805	0.98	21.06	6.71	1.09	16.06	19.80	25.47	5.618000e+01	▁▇▃▁▁
pc_Heating_Electric	0	1.00	7.80	8.71	0.00	2.54	4.96	9.85	9.028000e+01	▇▁▁▁▁
epc_total	0	1.00	434.07	188.87	30.00	315.00	390.00	503.00	6.350000e+03	▇▁▁▁▁
epc_newbuild	0	1.00	79.02	121.92	0.00	25.00	44.00	85.00	5.840000e+03	▇▁▁▁▁
epc_A	0	1.00	0.78	4.35	0.00	0.00	0.00	0.00	3.080000e+02	▇▁▁▁▁
epc_B	0	1.00	51.25	107.67	0.00	7.00	19.00	52.00	5.770000e+03	▇▁▁▁▁
epc_C	0	1.00	124.24	88.84	1.00	64.00	101.00	159.00	1.220000e+03	▇▁▁▁▁
epc_D	0	1.00	170.41	53.69	0.00	136.00	163.00	197.00	7.330000e+02	▅▇▁▁▁
epc_E	0	1.00	67.91	37.79	0.00	41.00	62.00	87.00	3.630000e+02	▇▅▁▁▁
epc_F	0	1.00	15.29	16.57	0.00	6.00	11.00	19.00	2.660000e+02	▇▁▁▁▁
epc_G	0	1.00	4.22	6.63	0.00	1.00	2.00	5.00	1.320000e+02	▇▁▁▁▁

## 
##         Adur    Allerdale Amber Valley         Arun     Ashfield      Ashford 
##           42           60           78           94           74           78

##                      region nLSOAs mean_KgCo2ePerCap sd_KgCo2ePerCap
## 1:                     East   3614          8952.239        3111.628
## 2:            East Midlands   2774          7863.378        3190.287
## 3:                   London   4835          9117.328        3137.922
## 4:               North East   1657          6827.127        4106.095
## 5:               North West   4497          7444.258        2859.758
## 6:               South East   5382          9871.440        3662.890
## 7:               South West   3281          7987.486        2758.576
## 8:            West Midlands   3487          7562.679        4014.465
## 9: Yorkshire and The Humber   3317          7449.515        2772.344

Now we need to convert the per capita to totals and then use the number of electricity meters as a proxy for the number of dwellings

Ideally we’d have Census 2021 data but we don’t have it yet. So instead we’ll use the number of electricity meters for 2018 which aligns with the CREDS data (might be an over-estimate if a dwelling has 2…)

First check the n electricity meters logic…

## LSOAs (check):

## [1] 32844

##               LSOA11NM           WD18NM nGasMeters nElecMeters epc_total
## 1:                <NA>             <NA>      83748       34561        NA
## 2: Aylesbury Vale 012A        Riverside       3373        3175      3110
## 3:    Test Valley 003B        St Mary's       2641        2487      2230
## 4:  Milton Keynes 017H        Broughton       2517        2382      2460
## 5:    Test Valley 003A          Alamein       2513        2638      2350
## 6:   Peterborough 019D Stanground South       2261        2178      1880

##               LSOA11NM                 WD18NM nGasMeters nElecMeters epc_total
## 1:                <NA>                   <NA>      83748       34561        NA
## 2:         Newham 013G Stratford and New Town        731        6351      6350
## 3:     Wandsworth 002B             Queenstown        675        3282      1700
## 4: Aylesbury Vale 012A              Riverside       3373        3175      3110
## 5:         Newham 037E            Royal Docks        574        3116      2900
## 6:       Lewisham 012E       Lewisham Central        568        2893      2730

##               LSOA11NM                 WD18NM nGasMeters nElecMeters epc_total
## 1: Aylesbury Vale 012A              Riverside       3373        3175      3110
## 2:    Test Valley 003B              St Mary's       2641        2487      2230
## 3:  Milton Keynes 017H              Broughton       2517        2382      2460
## 4:    Test Valley 003A                Alamein       2513        2638      2350
## 5:   Peterborough 019D       Stanground South       2261        2178      1880
## 6:        Swindon 008B Blunsdon and Highworth       2227        2166      2020

Check that the number of electricity meters reasonably correlates with the number of EPCs from the CREDS data. We would not expect the number of gas meters to correlate due to non-gas dwellings etc.

There may also be difficulties where there are multiple meters per property - e.g. one ‘standard’ and one ‘economy 7.’ Really should switch to using address counts from postcode file.

That assumption seems sensible…

Note very high number of meters in Newham…?

4.2.1 Estimate per dwelling emissions

We want to present the analysis in ‘per dwelling’ or ‘per household’ terms so we need to convert the total kg CO2e values to per dwelling values by dividing by the number of electricity meters.

## # Summary of per dwelling values

Table 4.3: Data summary
Name	…[]
Number of rows	32039
Number of columns	9
Key	NULL
_______________________
Column type frequency:
numeric	9
________________________
Group variables	None

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
CREDStotal_kgco2e_pdw	0	1	19490.24	9188.71	3587.62	12947.16	18275.82	24069.71	586372.22	▇▁▁▁▁
CREDSgas_kgco2e2018_pdw	0	1	2465.42	851.99	3.92	2037.62	2434.68	2868.68	71095.56	▇▁▁▁▁
CREDSelec_kgco2e2018_pdw	0	1	1021.63	220.17	40.55	888.82	977.15	1092.44	4046.23	▂▇▁▁▁
CREDSmeasuredHomeEnergy_kgco2e2018_pdw	0	1	3487.05	912.74	458.61	2978.41	3398.85	3894.92	72698.53	▇▁▁▁▁
CREDSotherEnergy_kgco2e2011_pdw	0	1	175.08	336.37	0.00	40.20	69.74	136.09	6877.09	▇▁▁▁▁
CREDSallHomeEnergy_kgco2e2018_pdw	0	1	3662.13	910.23	912.57	3125.71	3558.65	4082.50	76436.03	▇▁▁▁▁
CREDScar_kgco2e2018_pdw	0	1	2200.93	1038.37	127.70	1529.01	2142.16	2797.44	89700.00	▇▁▁▁▁
CREDSvan_kgco2e2018_pdw	1	1	366.16	2774.12	0.05	137.01	217.71	342.60	344822.80	▇▁▁▁▁
CREDSpersonalTransport_kgco2e2018_pdw	1	1	2567.13	2987.05	141.80	1742.05	2422.45	3151.56	346819.80	▇▁▁▁▁

Examine patterns of per dwelling emissions for sense.

4.2.1.1 All emissions

Figure 4.1 shows the LSOA level per dwelling ‘all emissions’ in Tonnes CO2e as estimated by the CREDS tool against the Index of Multiple Deprivation (IMD) score and uses the size of the points to represent the % of dwellings with electric heating. Colour is used to represent the IMD decile where decile 1 is the 10% least deprived.

## Per dwelling T CO2e - all emissions

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.1: Scatter of LSOA level all per dwelling emissions against IMD score

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## Correlation test (pwcorr)

## 
##  Pearson's product-moment correlation
## 
## data:  credsLsoaDT$IMDScore and credsLsoaDT$CREDStotal_kgco2e_pdw
## t = -123.51, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.5753081 -0.5604715
## sample estimates:
##        cor 
## -0.5679359

##    LSOA11CD            WD18NM          All_Tco2e_per_dw 
##  Length:32039       Length:32039       Min.   :  3.588  
##  Class :character   Class :character   1st Qu.: 12.947  
##  Mode  :character   Mode  :character   Median : 18.276  
##                                        Mean   : 19.490  
##                                        3rd Qu.: 24.070  
##                                        Max.   :586.372

##     LSOA11CD                  WD18NM All_Tco2e_per_dw
## 1: E01031998 Durrington and Larkhill         586.3722
## 2: E01009320                 Sheldon         364.6687
## 3: E01033484               Park East         203.6630
## 4: E01010151                  Knowle         171.2150
## 5: E01019556              Holmebrook         160.1703
## 6: E01033749               Greenbank         139.6909

##     LSOA11CD                 WD18NM All_Tco2e_per_dw
## 1: E01004562             Queenstown         4.965387
## 2: E01005133      Ancoats & Beswick         4.906386
## 3: E01008703                 Hendon         4.369222
## 4: E01015895               Victoria         4.289301
## 5: E01033726            Eltham West         3.808630
## 6: E01033583 Stratford and New Town         3.587624

4.2.1.2 Home energy use

Figure 4.2 uses the same plotting method to show emissions per dwelling due to gas use. This preserves the negative correlation shown in the previou splot for ‘all emissions’ but with some variation, notably for LSOAs which have a higher % ofelectric heating.

## Per dwelling T CO2e - gas emissions

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##     3.92  2037.62  2434.68  2465.42  2868.68 71095.56

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.2: Scatter of LSOA level gas per dwelling emissions against IMD score

## Saving 7 x 5 in image

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## Correlation test (pwcorr)

## 
##  Pearson's product-moment correlation
## 
## data:  credsLsoaDT$IMDScore and credsLsoaDT$CREDSgas_kgco2e2018_pdw
## t = -70.089, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.3740796 -0.3550910
## sample estimates:
##        cor 
## -0.3646232

Figure 4.3 uses the same plotting method to show emissions per dwelling due to electricity use. This is mnuch more random… although note the LSOAs with higher % electric heating.

## Per dwelling T CO2e - elec emissions

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.3: Scatter of LSOA level elec per dwelling emissions against IMD score - who emits?

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## Correlation test (pwcorr)

## 
##  Pearson's product-moment correlation
## 
## data:  credsLsoaDT$IMDScore and credsLsoaDT$CREDSelec_kgco2e2018_pdw
## t = -77.608, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.4069829 -0.3885483
## sample estimates:
##        cor 
## -0.3978058

Figure 4.4 uses the same plotting method to show emissions per dwelling due to other energy use. This should be higher for off-gas areas which tend to be rural areas so we also present analysis by the LSOA’s urban/rural classification.

## Per dwelling T CO2e - elec emissions

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.4: Scatter of LSOA level other energy per dwelling emissions against IMD score - who emits?

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## Correlation test (pwcorr)

## 
##  Pearson's product-moment correlation
## 
## data:  credsLsoaDT$IMDScore and credsLsoaDT$CREDSelec_kgco2e2018_pdw
## t = -77.608, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.4069829 -0.3885483
## sample estimates:
##        cor 
## -0.3978058

##                                              RUC11 mean_gas_kgco2e
## 1:                           Rural town and fringe        2536.798
## 2:       Rural town and fringe in a sparse setting        2254.050
## 3:                     Rural village and dispersed        1879.326
## 4: Rural village and dispersed in a sparse setting        1015.146
## 5:                             Urban city and town        2456.035
## 6:         Urban city and town in a sparse setting        2230.231
## 7:                         Urban major conurbation        2552.187
## 8:                         Urban minor conurbation        2582.837
##    mean_elec_kgco2e mean_other_energy_kgco2e
## 1:        1083.0125                274.22605
## 2:         993.6811                271.63854
## 3:        1481.8790               1131.91956
## 4:        1405.2387               1440.13693
## 5:         991.9263                 86.29202
## 6:         945.0026                124.64526
## 7:         981.2844                108.70527
## 8:         913.8924                123.97196

Check whether all measured energy emissions combined (gas & electricity) correlate with all emissions (in this data).

## 
##  Pearson's product-moment correlation
## 
## data:  credsLsoaDT$CREDStotal_kgco2e_pdw and credsLsoaDT$CREDSmeasuredHomeEnergy_kgco2e2018_pdw
## t = 158.14, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.6559143 0.6682142
## sample estimates:
##       cor 
## 0.6621088

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Strong correlkation. So in theory we could (currently) use measured energy emissions as a proxy for total emissions.

Repeat for all home energy - includes estimates of emissions from oil etc

## 
##  Pearson's product-moment correlation
## 
## data:  credsLsoaDT$CREDStotal_kgco2e_pdw and credsLsoaDT$CREDSallHomeEnergy_kgco2e2018_pdw
## t = 177.83, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.6992585 0.7102801
## sample estimates:
##       cor 
## 0.7048118

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Slightly weaker correlation…

4.2.1.3 Transport

We don’t expect to use this data as it is already taxed in a way that relates to emissions (?)

Figure 4.5 uses the same plotting method to show emissions per dwelling due to van use. Again, we present analysis by the LSOA’s urban/rural classification.

## Per dwelling T CO2e - car emissions

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.5: Scatter of LSOA level car use per dwelling emissions against IMD score

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## Correlation with IMD score (pwcorr)

## 
##  Pearson's product-moment correlation
## 
## data:  credsLsoaDT$IMDScore and credsLsoaDT$CREDScar_kgco2e2018_pdw
## t = -119.05, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.5613723 -0.5461891
## sample estimates:
##        cor 
## -0.5538267

##                                              RUC11 mean_car_kgco2e
## 1:                           Rural town and fringe        2882.600
## 2:       Rural town and fringe in a sparse setting        2198.057
## 3:                     Rural village and dispersed        3754.901
## 4: Rural village and dispersed in a sparse setting        3095.886
## 5:                             Urban city and town        2280.407
## 6:         Urban city and town in a sparse setting        1761.591
## 7:                         Urban major conurbation        1718.983
## 8:                         Urban minor conurbation        1899.379
##    mean_van_kgco2e
## 1:        412.7957
## 2:        346.9746
## 3:        664.4004
## 4:        586.5956
## 5:        379.2851
## 6:        300.1992
## 7:              NA
## 8:        307.5766

Figure 4.6 uses the same plotting method to show emissions per dwelling due to van use.

## Per dwelling T CO2e - van emissions

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.6: Scatter of LSOA level van use per dwelling emissions against IMD score

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## Correlation with IMD score (pwcorr)

## 
##  Pearson's product-moment correlation
## 
## data:  credsLsoaDT$IMDScore and credsLsoaDT$CREDSvan_kgco2e2018_pdw
## t = -0.79155, df = 32036, p-value = 0.4286
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.015371712  0.006528074
## sample estimates:
##          cor 
## -0.004422349

4.2.2 Impute EPC counts

In order to estimate the LSOA level retrofit costs, we need to impute the EPC counts in each LSOA. We do this using the number of electricity meters as the presumed number of dwellings and the observed % of EPCs in each band for all dwellings with EPCs which is provided by the CREDS data. This assumes that if we had EPCs for all dwellings then the % in each band in each LSOA would stay the same. This is quite a bold assumption…

Note that the EPC database is continuously updated so more recent upgrades will not be captured in the data used for this analysis. This means the total retrofit costs are likely to be an over-estimate. The extent of this over-estimate would require the use of an updated (current) EPC data extract and is left for future work.

## N EPCs

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    30.0   315.0   390.0   434.2   503.0  6350.0

## N elec meters

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    36.0   623.0   692.0   736.3   809.0  6351.0

Correlation between high % EPC F/G or A/B and deprivation?

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Now we need to convert the % to dwellings using the number of electricity meters (see above).

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

4.2.3 Estimating the annual emissions levy

Case studies:

Annual carbon tax
Half-hourly (real time) carbon tax (not implemented) - this would only affect electricity

BEIS/ETC Carbon ‘price’

2020 - £21.93 https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/849276/Carbon__Penalty_Price_Determination_for_2020.pdf

EU carbon ‘price’

September 2021: €60 (£51) https://tradingeconomics.com/eecxm:ind

BEIS Carbon ‘Value’ https://www.gov.uk/government/publications/valuing-greenhouse-gas-emissions-in-policy-appraisal/valuation-of-greenhouse-gas-emissions-for-policy-appraisal-and-evaluation#annex-1-carbon-values-in-2020-prices-per-tonne-of-co2

based on a Marginal Abatement Cost (MAC)
2021:
- Low: £122/T
- Central: £245/T <- use the central value for now
- High: £367/T

Scenario 1: apply the central value Scenario 2: apply the low/central/high as a rising block tariff for each emissions source. Set threhsolds to 33% and 66% (in absence of any other guidance!)

4.2.3.1 Scenario 1: Central cost

The table below shows the overall £ GBP total for the case study area in £M.

## £m

##    nLSOAs beis_GBPtotal_c beis_total_c_gas beis_GBPtotal_c_elec
## 1:  32039        107100.4          13847.3               5871.4

## £m

##                      region nLSOAs beis_GBPtotal_c beis_total_c_gas
## 1:               South East   5278       20772.109        2272.0667
## 2:                   London   4826       19038.076        2048.9049
## 3:               North West   4463       12703.630        1952.4218
## 4:                     East   3392       12199.273        1450.0085
## 5:            West Midlands   3403       10191.715        1475.8156
## 6:               South West   3059        9784.332        1147.1720
## 7: Yorkshire and The Humber   3271        9495.299        1494.0830
## 8:            East Midlands   2713        8687.056        1249.5031
## 9:               North East   1634        4228.912         757.3235
##    beis_GBPtotal_c_elec
## 1:            1038.8330
## 2:             870.4882
## 3:             766.0786
## 4:             668.5684
## 5:             604.8308
## 6:             613.8662
## 7:             550.1641
## 8:             503.9008
## 9:             254.6698

The table below shows the mean per dwelling value rounded to the nearest £10.

##    beis_GBPtotal_c_perdw beis_GBPtotal_c_gas_perdw beis_GBPtotal_c_elec_perdw
## 1:                  4780                       600                        250
##    beis_GBPtotal_c_energy_perdw
## 1:                          850

Figure ?? shows the total £k per LSOA and £ per dwelling revenue using BEIS central carbon price plotted against IMD score. The tables show the LSOAs with the highest and lowest values.

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.7: £k per LSOA revenue using BEIS central carbon price

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.8: £k per LSOA revenue using BEIS central carbon price

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     879    3172    4478    4775    5897  143661

LSOA11CD	LSOA01NM	WD18NM	IMD_Decile_label	CREDStotal_kgco2e_pdw	beis_GBPtotal_c_perdw
E01031998	Wiltshire 045C	Durrington and Larkhill	9	586,372.2	143,661.19
E01009320	Birmingham 081F	Sheldon	4	364,668.7	89,343.84
E01033484	Darlington 008F	Park East	1 (10% most deprived)	203,663.0	49,897.44
E01010151	Solihull 026A	Knowle	8	171,215.0	41,947.69
E01019556	Chesterfield 010C	Holmebrook	2	160,170.3	39,241.73
E01033749	Liverpool 042F	Greenbank	6	139,690.9	34,224.27

LSOA11CD	LSOA01NM	WD18NM	IMD_Decile_label	CREDStotal_kgco2e_pdw	beis_GBPtotal_c_perdw
E01004562	Wandsworth 002B	Queenstown	3	4,965.387	1,216.5198
E01005133	Manchester 013D	Ancoats & Beswick	1 (10% most deprived)	4,906.386	1,202.0645
E01008703	Sunderland 013B	Hendon	1 (10% most deprived)	4,369.222	1,070.4593
E01015895	Southend-on-Sea 010A	Victoria	1 (10% most deprived)	4,289.301	1,050.8787
E01033726	Greenwich 034E	Eltham West	2	3,808.630	933.1143
E01033583	Newham 013G	Stratford and New Town	3	3,587.624	878.9679

Figure ?? repeats the analysis but just for gas.

Now we see why Durrington & Larkhill stands out - either there is a decimal point error or there is a lot of ‘residential’ gas being used in the army camp and it’s all being allocated to one LSOA :-)

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.9: £k per LSOA incurred via gas using BEIS central carbon price

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.10: £k per LSOA incurred via gas using BEIS central carbon price

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##     0.96   499.22   596.50   604.03   702.83 17418.41

LSOA11CD	LSOA01NM	WD18NM	IMD_Decile_label	gasTCO2e_pdw	beis_GBPtotal_c_gas_perdw
E01031998	Wiltshire 045C	Durrington and Larkhill	9	71.095556	17,418.411
E01000213	Barnet 033F	Garden Suburb	9	7.355417	1,802.077
E01023812	Three Rivers 004A	Chorleywood North & Sarratt	10 (10% least deprived)	7.167803	1,756.112
E01023841	Three Rivers 011C	Moor Park & Eastbury	10 (10% least deprived)	6.925828	1,696.828
E01004114	Sutton 025D	Cheam	10 (10% least deprived)	6.721036	1,646.654
E01023813	Three Rivers 004B	Chorleywood North & Sarratt	10 (10% least deprived)	6.718669	1,646.074

LSOA11CD	LSOA01NM	WD18NM	IMD_Decile_label	gasTCO2e_pdw	beis_GBPtotal_c_gas_perdw
E01026645	King's Lynn and West Norfolk 002A	Brancaster	4	0.015725987	3.8528667
E01026718	King's Lynn and West Norfolk 004D	Valley Hill	5	0.014207424	3.4808188
E01027382	Northumberland 002D	Norham and Islandshires	4	0.013286252	3.2551318
E01020864	County Durham 064G	Evenwood	5	0.013174354	3.2277167
E01032746	Southampton 029F	Bargate	7	0.012818095	3.1404333
E01020534	West Dorset 003F	Maiden Newton	5	0.003918875	0.9601244

Figure ?? repeats the analysis for electricity.

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.11: £k per LSOA incurred via electricity using BEIS central carbon price

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.12: £k per LSOA incurred via electricity using BEIS central carbon price

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   9.934 217.760 239.403 250.299 267.648 991.327

LSOA11CD	LSOA01NM	WD18NM	IMD_Decile_label	elecTCO2e_pdw	beis_GBPtotal_c_elec_perdw
E01000206	Barnet 033B	Garden Suburb	8	4.046235	991.3275
E01030692	Runnymede 005D	Virginia Water	9	3.360000	823.2000
E01030342	Elmbridge 018B	Oxshott and Stoke D'Abernon	10 (10% least deprived)	3.346058	819.7842
E01030346	Elmbridge 016A	Weybridge St George's Hill	8	3.280690	803.7690
E01004690	Westminster 019D	Knightsbridge and Belgravia	10 (10% least deprived)	2.875978	704.6145
E01003465	Merton 002D	Village	10 (10% least deprived)	2.873194	703.9325

LSOA11CD	LSOA01NM	WD18NM	IMD_Decile_label	elecTCO2e_pdw	beis_GBPtotal_c_elec_perdw
E01008777	Sunderland 026C	St Chad's	1 (10% most deprived)	0.50798472	124.456256
E01024604	Swale 014C	St Ann's	2	0.48269076	118.259237
E01002862	Kensington and Chelsea 014E	Stanley	4	0.45468354	111.397468
E01033736	Greenwich 004H	Woolwich Riverside	8	0.43406378	106.345626
E01004739	Westminster 024E	Tachbrook	8	0.33211144	81.367302
E01010257	Walsall 007E	Aldridge North and Walsall Wood	9	0.04054826	9.934324

Figure ?? shows the same analysis for measured energy (elec + gas)

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.13: £k per LSOA incurred via electricity and gas using BEIS central carbon price

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.14: £k per LSOA incurred via electricity and gas using BEIS central carbon price

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   112.4   729.7   832.7   854.3   954.3 17811.1

4.2.3.2 Scenario 2: Rising block tariff

Applied at to per dwelling values (not LSOA total)

Cut at 25%, 50% - so any emissions over 50% get high carbon cost

## Cuts for total per dw

##         0%        25%        50%        75%       100% 
##   3587.624  12947.165  18275.816  24069.709 586372.222

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

##           V1 beis_GBPtotal_sc2_l_perdw beis_GBPtotal_sc2_c_perdw
##  1: 20.14367                  1579.554                 1305.5194
##  2: 18.65717                  1579.554                 1305.5194
##  3: 12.73055                  1553.127                    0.0000
##  4: 19.87204                  1579.554                 1305.5194
##  5: 28.94094                  1579.554                 1305.5194
##  6: 15.12282                  1579.554                  533.0367
##  7: 20.44254                  1579.554                 1305.5194
##  8: 21.00183                  1579.554                 1305.5194
##  9: 11.91349                  1453.446                    0.0000
## 10: 27.68047                  1579.554                 1305.5194
##     beis_GBPtotal_sc2_h_perdw beis_GBPtotal_sc2_perdw
##  1:                  685.5016                3570.575
##  2:                  139.9562                3025.030
##  3:                    0.0000                1553.127
##  4:                  585.8127                3470.886
##  5:                 3914.1019                6799.175
##  6:                    0.0000                2112.591
##  7:                  795.1884                3680.262
##  8:                 1000.4491                3885.523
##  9:                    0.0000                1453.446
## 10:                 3451.5071                6336.581

Table 4.4: Data summary
Name	…[]
Number of rows	32039
Number of columns	3
Key	NULL
_______________________
Column type frequency:
numeric	3
________________________
Group variables	None

Variable type: numeric

skim_variable	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
V1	1	19.49	9.19	3.59	12.95	18.28	24.07	586.37	▇▁▁▁▁
beis_GBPtotal_sc2_perdw	1	3727.91	3031.87	437.69	1579.54	2885.00	5011.43	211376.45	▇▁▁▁▁
beis_GBPtotal_sc2	1	2528423.89	1653321.47	543095.20	1222262.12	2220433.98	3356216.64	84377314.16	▇▁▁▁▁

##    nLSOAs sum_total_sc1 sum_total_sc2
## 1:  32039      107100.4      81008.17

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## Saving 7 x 5 in image

##    CREDSgas_kgco2e2018_pdw beis_GBPgas_sc2_l_perdw
## 1:                130.9338                15.97393
## 2:                 82.7677                10.09766
## 3:                717.3671                87.51878
## 4:               1041.0619               127.00956
## 5:               2480.0943               248.59012
## 6:                793.2446                96.77584

##    CREDSgas_kgco2e2018_pdw beis_GBPgas_sc2_l_perdw beis_GBPgas_sc2_c_perdw
## 1:                130.9338                15.97393                 0.00000
## 2:                 82.7677                10.09766                 0.00000
## 3:                717.3671                87.51878                 0.00000
## 4:               1041.0619               127.00956                 0.00000
## 5:               2480.0943               248.59012                97.27961
## 6:                793.2446                96.77584                 0.00000
##    beis_GBPgas_sc2_h_perdw beis_GBPgas_sc2_perdw
## 1:                 0.00000              15.97393
## 2:                 0.00000              10.09766
## 3:                 0.00000              87.51878
## 4:                 0.00000             127.00956
## 5:                16.66576             362.53549
## 6:                 0.00000              96.77584

## [1] 9086.681

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## [1] 3997.045

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## £m

##    nLSOAs sumAllConsEmissions_GBP sumGasEmissions_GBP sumElecEmissions_GBP
## 1:  32039                81008.17            9086.681             3997.045
##      sumPop
## 1: 54619583

## £m

##                      region nLSOAs sumAllConsEmissions_GBP sumGasEmissions_GBP
## 1:               South East   5278               17507.695           1503.8874
## 2:                   London   4826               15649.748           1389.3724
## 3:                     East   3392                9526.267            939.0531
## 4:               North West   4463                8777.331           1282.6361
## 5:            West Midlands   3403                7270.355            976.6076
## 6:               South West   3059                6809.964            653.9993
## 7: Yorkshire and The Humber   3271                6504.827           1011.0844
## 8:            East Midlands   2713                6247.711            827.1231
## 9:               North East   1634                2714.274            502.9172
##    sumElecEmissions_GBP  sumPop
## 1:             766.6684 8973952
## 2:             583.7890 8889572
## 3:             487.3759 5818700
## 4:             491.5296 7236660
## 5:             410.8372 5765703
## 6:             431.0802 5213266
## 7:             342.8749 5405939
## 8:             339.4664 4693551
## 9:             143.4232 2622240

4.2.4 Estimate retofit costs

fromAE <- 13300 fromFG <- 26800

Excludes EPC A, B & C (assumes no need to upgrade)

## To retrofit D-E (£m)

## [1] 177847.9

## Number of dwellings: 13372024

## To retrofit F-G (£m)

## [1] 26752.52

## Number of dwellings: 998229

## To retrofit D-G (£m)

## [1] 204600.4

## To retrofit D-G (mean per dwelling)

## [1] 14163.45

##    meanPerLSOA_GBPm total_GBPm
## 1:         6.385981   204600.4

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

4.2.5 Compare levy with costs

4.2.5.1 Scenario 1

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Repeat per dwelling

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

4.2.5.2 Scenario 2

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Repeat per dwelling

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

4.2.6 Years to pay…

4.2.6.1 Scenario 1

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
##  0.09599  2.40262  3.17210  3.53055  4.44332 15.64765        1

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
##   0.7742  14.7584  16.8635  17.5709  19.2684 118.6634        1

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

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## Highest retofit sum cost

##      LSOA11CD            LSOA11NM                             WD18NM
##  1: E01019012       Cornwall 054E                       St Ives East
##  2: E01018781       Cornwall 034B                    Rame Peninsular
##  3: E01027840    Scarborough 002C                           Mulgrave
##  4: E01021988       Tendring 018A                         Golf Green
##  5: E01018766       Cornwall 028D Looe West, Lansallos and Lanteglos
##  6: E01020541    West Dorset 002C                     Sherborne East
##  7: E01026741  North Norfolk 004A                         High Heath
##  8: E01019002       Cornwall 070B               Newlyn and Mousehole
##  9: E01018982       Cornwall 057C                        Hayle North
## 10: E01027374 Northumberland 003A                           Bamburgh
##     retrofitSum yearsToPay  epc_D_pc  epc_E_pc  epc_F_pc   epc_G_pc
##  1:    26389383   32.33181 0.2881890 0.2251969 0.1314961 0.07086614
##  2:    22060172   49.74697 0.2993730 0.2664577 0.2335423 0.10971787
##  3:    21959636   32.73446 0.2821317 0.2272727 0.2163009 0.10031348
##  4:    21701517   36.61775 0.2955900 0.3313468 0.1620977 0.14302741
##  5:    21409249   46.57111 0.2181070 0.2716049 0.2935528 0.10973937
##  6:    21066562   31.46956 0.3038793 0.3232759 0.2090517 0.06896552
##  7:    20793004   35.15440 0.2971888 0.2650602 0.1994645 0.06827309
##  8:    20415414   45.01341 0.1710963 0.2807309 0.3089701 0.17940199
##  9:    20411151   40.50980 0.2675386 0.1819263 0.2092747 0.14030916
## 10:    19563519   29.95735 0.3329532 0.2257697 0.1402509 0.05131129

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What happens in Year 2 totally depends on the rate of upgrades…

4.2.6.2 Scenario 2

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
##  0.06524  2.83119  4.88954  5.92627  8.83376 31.42356        1

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
##   0.7742  14.7584  16.8635  17.5709  19.2684 118.6634        1

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## Saving 7 x 5 in image

## Saving 7 x 5 in image

What happens in Year 2 totally depends on the rate of upgrades…

4.2.6.3 Compare scenarios

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5 R environment

5.1 R packages used

base R (R Core Team 2016)
bookdown (Xie 2016a)
data.table (Dowle et al. 2015)
ggplot2 (Wickham 2009)
kableExtra (Zhu 2018)
knitr (Xie 2016b)
rmarkdown (Allaire et al. 2018)
skimr (Arino de la Rubia et al. 2017)

5.2 Session info

6 Data Tables

I don’t know if this will work…

## Doesn't

References

Allaire, JJ, Yihui Xie, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, Hadley Wickham, Joe Cheng, and Winston Chang. 2018. Rmarkdown: Dynamic Documents for r. https://CRAN.R-project.org/package=rmarkdown.

Arino de la Rubia, Eduardo, Hao Zhu, Shannon Ellis, Elin Waring, and Michael Quinn. 2017. Skimr: Skimr. https://github.com/ropenscilabs/skimr.

Dowle, M, A Srinivasan, T Short, S Lianoglou with contributions from R Saporta, and E Antonyan. 2015. Data.table: Extension of Data.frame. https://CRAN.R-project.org/package=data.table.

R Core Team. 2016. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Wickham, Hadley. 2009. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. http://ggplot2.org.

Xie, Yihui. 2016a. Bookdown: Authoring Books and Technical Documents with R Markdown. Boca Raton, Florida: Chapman; Hall/CRC. https://github.com/rstudio/bookdown.

———. 2016b. Knitr: A General-Purpose Package for Dynamic Report Generation in r. https://CRAN.R-project.org/package=knitr.

Zhu, Hao. 2018. kableExtra: Construct Complex Table with ’Kable’ and Pipe Syntax. https://CRAN.R-project.org/package=kableExtra.

Simulating a local emissions levy to fund local energy effiency retrofit

All LSOAs (England?)

Ben Anderson (b.anderson@soton.ac.uk)

Last run at: 2021-10-20 11:09:15

1 About

2 Citation

3 Introduction

4 Emissions Levy Case Study - All LSOAs

4.1 Data

4.1.1 Useful LSOA labels and codes

4.1.2 IMD 2019

4.1.3 Fuel poverty

4.2 CREDS place-based emmissions estimates

4.2.1 Estimate per dwelling emissions

4.2.1.1 All emissions

4.2.1.2 Home energy use

4.2.1.3 Transport

4.2.2 Impute EPC counts

4.2.3 Estimating the annual emissions levy

4.2.3.1 Scenario 1: Central cost

4.2.3.2 Scenario 2: Rising block tariff

4.2.4 Estimate retofit costs

4.2.5 Compare levy with costs

4.2.5.1 Scenario 1

4.2.5.2 Scenario 2

4.2.6 Years to pay…

4.2.6.1 Scenario 1

4.2.6.2 Scenario 2

4.2.6.3 Compare scenarios

5 R environment

5.1 R packages used

5.2 Session info

6 Data Tables

References